The novelty of this Bachelor’s thesis is to test expediency of thematic maps in Estonia’s
real estate market analysis. Thematic maps may consist of a large amount of information.
After visualization, it is easy to understand which local government has transaction
numbers low or high and where the prices are increasing or decreasing. Tables are full of
intense information and often it is impossible to distinguish important information from
unimportant.
The purpose of this Bachelor’s thesis is to test the functional usage and expediency of
thematic maps in real estate market analysis. There is one question, which needs to be
answered. Will the use of thematic map provide a clearer and quicker overview about the
processes of the real estate market compared to the information in the table?
The research object is purchase-sale transactions of apartments in three time periods (2004
– 2006, 2009 – 2011, 2014 – 2016). Thematic maps describe the activity of transactions,
average prices of square meters and compare the changes of average prices of square
meters in three time periods.
The data of real property price statistics is collected from Estonian Land Board and data
about population is collected from Statistics Estonia. Thematic maps were prepared with
commonly used GIS software ArcGIS. Thematic maps base on the map layer of local
government boundaries. In additon, This map layer combined with the tabel of real estate
transactions’s data which was previously composed in Microsoft Excel.
As a result was prepared 30 thematic maps, which show the number of transactions,
number of transactions per 1000 inhabitants and average price of a square meter. In
addition, there are thematic maps, which show changes of average prices of square meters.
17 thematic maps are in main text and 13 maps are in appendixes. In the main text are the
maps which are more informative. It depends on the data classification methods and
number of classes.
This Bachelor’s work can be further developed. It would be possible to examine how the
activity of transactions were changed during specific timeperiods. It is also possible to test
ohter data classification methods.